ANN-based modelling of peppermint flavour encapsulation process with ultrasound approach

Encapsulation has great potential for preserving the flavour and health benefits of bioactive compounds. Hence, in the previous study, an attempt was made to encapsulate peppermint flavour in a gum arabic (GA) shell. To further understand the effect of a wide range of parameters, in the present study, artificial neural networks (ANNs) are developed. To predict the effect of various parameters on the encapsulation process, networks are developed with a back-propagation algorithm. Input parameters for the ANN are flavour concentration, GA concentration, spray dryer temperature and feed flow rate to the spray dryer. The encapsulation process is evaluated in terms of encapsulation efficiency, product yield, and particle size. To predict all outputs simultaneously, a combined model is developed. The results showed that the combined model has similar accuracy as that of the individual model and also helps to save on processing time. For the combined model, the best prediction performance is obtained with 5-4-3 ANN architecture exhibiting an R-2 value of 0.9991, and corresponding MSE values of 0.000 54, 0.000 63, and 0.000 61 for encapsulation efficiency, product yield, and particle size, respectively. This indicates that the developed ANN model is capable of predicting the encapsulation process. The interpolation and extrapolation ability of the developed network is also evaluated.